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agents in their daily work, enabling a comparatively small team to take on work that would traditionally require 
a far larger one by multiplying the impact of each person. 

The  expansion  of  our  own  use  of  Claude  reflects  this  leverage  directly. The  value  of  our  own  internal  Claude 
usage at list prices has grown by over 250 times from January 2026 to September 2026, and in April 2026, this 
monthly  value  first  exceeded  our  monthly  personnel  expense,  excluding  stock-based  compensation. Because 
each  version  of  Claude  is  used  to  help  build  the  next  and  because  Claude  makes  our  teams  more  productive 
across the company, we believe the compute we devote to internal usage compounds these gains, continuously 
accelerating our pace of development and further strengthening our leadership position.

Using  AI  to  help  build  more  capable  AI  could  also  pose  a  considerable  safety  risk,  particularly  if  it  sharply 
accelerates the pace of progress. This is one of the areas covered by our Responsible Scaling Policy, which sets 
out  our  plans  to  monitor  and  mitigate  the  risks  as  we  approach  what  has  been  termed  “recursive  self-
improvement.”

AI is Transforming Intelligence 

We believe that Powerful AI, a highly capable and impactful form of frontier AI characterized by the following 
key attributes, will become widely available within a few years:

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cognitive capabilities matching or exceeding those of top human experts across most professional and 
scientific disciplines;

the ability to navigate all interfaces available to a human working virtually, including text, audio, video, 
and direct computer control;

the ability to reason through complex tasks autonomously over extended periods—lasting hours, days, 
or weeks—and seek clarification and feedback when needed; 

the  ability  to  coordinate  across  multiple  instances  and  collaborate  with  both  humans  and  other  AI 
systems; and

the  ability  to  interface  with  the  physical  world  by  digitally  controlling  laboratory  equipment,  robotic 
systems, and other machines through digital connections.

If Powerful AI is directed at solving humanity’s hardest problems and the risks are managed responsibly, the 
potential  is  extraordinary.  The  possibilities  include  compressing  decades  of  scientific  progress  into  years; 
accelerating drug discovery, disease detection, and the delivery of personalized therapy; providing individually 
tailored education and expert financial and legal advice; advancing power grids and battery design for greater 
efficiency  and  reliability;  and  extending  to  the  developing  world  capabilities  that  took  wealthy  nations 
generations to build.

Scaling Laws and the Predictability of Progress

The intelligence and capabilities of AI systems have advanced dramatically. Tasks that were beyond the reach of 
AI systems just a few years ago, such as complex mathematical reasoning, designing scientific experiments, and 
executing substantial portions of software development, are now routinely performed by AI. 

These  advances  have  followed  empirical  “Scaling  Laws,”  the  finding  that  AI  model  performance  improves 
predictably with increases in model size, training data, and compute. The foundational paper, Scaling Laws for 
Neural Language Models, whose lead authors (including Dario Amodei, Jared Kaplan, Sam McCandlish, and 
Tom Brown) later co-founded Anthropic, was critical in establishing these relationships and helped guide the 
wave of modern LLMs. 

We  expect  AI  capabilities—across  language,  reasoning,  mathematics,  coding,  scientific  analysis,  and  an 
expanding range of agentic tasks and knowledge work—to continue advancing for many years. As a result, we 
believe this will drive demand for AI that outstrips supply for the foreseeable future, limited principally by the 
availability  of  compute.  We  believe  that  the  majority  of  this  demand  will  be  concentrated  at  the  frontier:  our 
experience has been that enterprises gravitate to our most capable models across a wide variety of workloads, 

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